Real-Time Fluid Identification From Integrating Advanced Mud Gas and Petrophysical Logs

Abstract

Advanced mud gas logging has been used in the oil industry for about 25 years. However, it has been challenging to predict reservoir fluid properties quantitatively (e.g., gas oil ratio – GOR) from only the advanced mud gas data (AMG) while drilling. Yang et al. proposed the first accurate GOR predictive model in 2019 from advanced surface data based on a machine learning algorithm. Since then, the method has been applied to both conventional and unconventional fields with good results. For our Norwegian operational units, we are developing a real-time service for fluid identification to optimize fluid sampling in exploration wells and support production drilling. Here, quantitative information about reservoir fluids will support the teams to take wellinformed decisions with respect to well placement, petrophysical log interpretation, and optimizing production by improving the selection of perforation intervals. We utilize a standard wellbore software platform to integrate the following data for fluid identification: AMG data, various AMG QC tools, normalized total gas response, GOR prediction, and petrophysical logs from logging while drilling (LWD). The proposed work approach integrates the information from multiple disciplines and makes the real-time fluid identification task much more reliable for operational decisions. We selected two field cases to demonstrate the approach of integrating AMG data and petrophysical logs. The first field case is an exploration well with multiple reservoir zones planned as production targets. The integrated approach shows reservoir fluids from all reservoir zones are almost identical. Consequently, we reduced the sampling program and only sampled at the best reservoir zone for cost efficiency. The second field case is a mature field being produced by pressure support from water, gas or water alternating gas injections. When a new production well is drilled, there is always a question of whether it encounters any injection gas. We applied the new approach to several production wells and obtained satisfying result. The latest information from the predictive GOR model solved many puzzles in petrophysical interpretations. This paper presents a new approach for reservoir fluid identification by integrating advanced mud gas data and petrophysical logs while drilling. This new approach makes real-time operational adjustments possible based on reservoir fluid identification along the well. The business potential is significant for accurately mapping resources for in-fill wells, boosting profitability, and lowering carbon footprint.